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Elements of Multivariate Time Series Analysis

  • Textbook
  • © 1993

Overview

Authors:
  1. Gregory C. Reinsel

    1. Department of Statistics, University of Wisconsin, Madison, Madison, USA

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Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Vector Time Series and Model Representations

    • Gregory C. Reinsel
    Pages 1-20

  3. Vector ARMA Time Series Models and Forecasting

    • Gregory C. Reinsel
    Pages 21-51

  4. Canonical Structure of Vector ARMA Models

    • Gregory C. Reinsel
    Pages 52-73

  5. Initial Model Building and Least Squares Estimation for Vector AR Models

    • Gregory C. Reinsel
    Pages 74-110

  6. Maximum Likelihood Estimation and Model Checking for Vector ARMA Models

    • Gregory C. Reinsel
    Pages 111-153

  7. Reduced-Rank and Nonstationary Co-Integrated Models

    • Gregory C. Reinsel
    Pages 154-191

  8. State-Space Models, Kalman Filtering, and Related Topics

    • Gregory C. Reinsel
    Pages 192-225

  9. Back Matter

    Pages 226-264
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Keywords

About this book

The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto­ correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others.

Authors and Affiliations

  • Department of Statistics, University of Wisconsin, Madison, Madison, USA

    Gregory C. Reinsel

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